Modeling and optimization of nano-rod plasmonic sensor by adaptive neuro fuzzy inference system (ANFIS)

Ganji, Jabbar; Kaboli, Milad; Tabatabaee, Seyed Sajjad; Shabani, Pejman

doi:10.1007/s11082-020-02675-0

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The geometry parameter inputs include the number, type, position, dimension, and orientation of the shapes contained in each 2D image, which allows high freedom in designing arbitrarily shaped devices. Specifically, a type of residual CNN-ResNet was used, which contains multiple sequentially connected units made of a convolutional layer, a normalization layer (batch), and an activation layer Similarly, Ganji et al conducted an application of ANFIS (adaptive neuro fuzzy inference system) on predicting the LSPR response of plasmonic nanorods given the dimensional parameters including diameter, height, curvature value, and periodicity [85]. Arzola et al focused on the effect of the gold concave nano-cubes topology on the location of the surface plasmon resonance based on the extinction spectra.…”

Section: For Property-predictionmentioning

confidence: 99%

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Aggarwal

Shankar

2022

Nanomaterials

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Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder–Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.

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Section: For Property-predictionmentioning

confidence: 99%